A Generative Model for Dynamic Networks with Applications


  • Shubham Gupta Indian Institute of Science
  • Gaurav Sharma Indian Institute of Science
  • Ambedkar Dukkipati Indian Institute of Science




Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.




How to Cite

Gupta, S., Sharma, G., & Dukkipati, A. (2019). A Generative Model for Dynamic Networks with Applications. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7842-7849. https://doi.org/10.1609/aaai.v33i01.33017842



AAAI Technical Track: Reasoning under Uncertainty